{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from tqdm import tqdm, tqdm_notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sns.set_context(\"poster\")\n",
    "sns.set_style(\"ticks\")\n",
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_neurons = 10\n",
    "num_layers = 3\n",
    "max_length = 100\n",
    "input_size = 1\n",
    "output_size = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def variable_summaries(var, name):\n",
    "    \"\"\"Attach a lot of summaries to a Tensor.\"\"\"\n",
    "    with tf.name_scope('summaries'):\n",
    "        mean = tf.reduce_mean(var)\n",
    "        tf.scalar_summary('mean/' + name, mean)\n",
    "    with tf.name_scope('stddev'):\n",
    "        stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))\n",
    "    tf.scalar_summary('sttdev/' + name, stddev)\n",
    "    tf.scalar_summary('max/' + name, tf.reduce_max(var))\n",
    "    tf.scalar_summary('min/' + name, tf.reduce_min(var))\n",
    "    tf.histogram_summary(name, var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SequenceModel(object):\n",
    "    def __init__(self, num_neurons, num_layers, max_length, input_size, output_size):\n",
    "        self.num_neurons = num_neurons\n",
    "        self.num_layers = num_layers\n",
    "        self.max_length = max_length\n",
    "        self.input_size = input_size\n",
    "        self.output_size = output_size\n",
    "        self._gen_model()\n",
    "\n",
    "    def _gen_model(self):\n",
    "        self.dropout = tf.placeholder(tf.float32)\n",
    "        self.cell = tf.nn.rnn_cell.LSTMCell(self.num_neurons, state_is_tuple=True)\n",
    "        self.cell = tf.nn.rnn_cell.DropoutWrapper(self.cell, output_keep_prob=self.dropout)\n",
    "        self.cell = tf.nn.rnn_cell.MultiRNNCell([self.cell]*self.num_layers, state_is_tuple=True)\n",
    "        self.data = tf.placeholder(tf.float32, [None, self.max_length, self.input_size], name=\"input_data\")\n",
    "        self.weight = tf.Variable(tf.truncated_normal([self.num_neurons, self.output_size], stddev=0.01), name=\"weights\")\n",
    "        self.bias = tf.Variable(tf.constant(0.1, shape=[self.output_size]), name=\"bias\")\n",
    "        self.output, self.state = tf.nn.dynamic_rnn(self.cell, self.data, dtype=tf.float32)\n",
    "        print self.weight.get_shape(), self.bias.get_shape(), self.output.get_shape()\n",
    "        self.predictions = tf.matmul(tf.reshape(self.output, [-1, self.num_neurons]), self.weight) + self.bias\n",
    "        self.predictions = tf.reshape(self.predictions, [-1, self.max_length, self.output_size])\n",
    "        print self.output.get_shape(), self.predictions.get_shape()\n",
    "        self.labels = tf.placeholder(tf.float32, [None, self.max_length, self.output_size])\n",
    "        self.cost = tf.reduce_mean(tf.reduce_sum(tf.square(self.labels-self.predictions), reduction_indices=[1]))\n",
    "        self.optimizer = tf.train.RMSPropOptimizer(0.01).minimize(self.cost)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tf.python.framework.ops.reset_default_graph()\n",
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 1) (1,) (?, 100, 10)\n",
      "(?, 100, 10) (?, 100, 1)\n"
     ]
    }
   ],
   "source": [
    "model = SequenceModel(num_neurons, num_layers, max_length, input_size, output_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.run(tf.initialize_all_variables())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1, 100, 1), (1, 100, 1))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = 1\n",
    "x = np.array([np.linspace(-2, 2, num=max_length)[:, np.newaxis] for i in xrange(N)])\n",
    "y = np.exp(x)\n",
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Ro+3VAgAAAAA4MyuvV7Ti6BfYsHFj568ND5fOOUc6csRePQAAAACA9ll70gGwobxcuuee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\nmh4BprBQWrLEt3s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3br2vgaUsbm5u0Gq1subUl2XmzJlISUnBgwcP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\n1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d08311c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,5, sharex=True, sharey=False, figsize=(15, 6))\n",
    "ax = ax.flatten()\n",
    "for i in xrange(x.shape[0]):\n",
    "    ax[i].plot(x[i, :, 0], y[i, :, 0], \"bo\")\n",
    "    \n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost: 243.630\n",
      "Cost: 281.543\n",
      "Cost: 191.777\n",
      "Cost: 165.435\n",
      "Cost: 151.189\n",
      "Cost: 208.458\n",
      "Cost: 236.159\n",
      "Cost: 165.464\n",
      "Cost: 197.491\n",
      "Cost: 231.745\n",
      "Cost: 245.712\n",
      "Cost: 280.075\n",
      "Cost: 189.489\n",
      "Cost: 258.715\n",
      "Cost: 212.815\n",
      "Cost: 213.596\n",
      "Cost: 175.838\n",
      "Cost: 208.578\n",
      "Cost: 207.583\n",
      "Cost: 208.471\n",
      "Cost: 223.685\n",
      "Cost: 228.321\n",
      "Cost: 166.584\n",
      "Cost: 218.225\n",
      "Cost: 146.425\n",
      "Cost: 210.301\n",
      "Cost: 181.365\n",
      "Cost: 264.536\n",
      "Cost: 177.047\n",
      "Cost: 134.752\n",
      "Cost: 259.609\n",
      "Cost: 261.562\n",
      "Cost: 199.340\n",
      "Cost: 216.860\n",
      "Cost: 241.115\n",
      "Cost: 200.299\n",
      "Cost: 180.526\n",
      "Cost: 213.483\n",
      "Cost: 156.912\n",
      "Cost: 209.787\n",
      "Cost: 210.995\n",
      "Cost: 204.536\n",
      "Cost: 222.581\n",
      "Cost: 220.950\n",
      "Cost: 192.564\n",
      "Cost: 167.316\n",
      "Cost: 223.811\n",
      "Cost: 280.984\n",
      "Cost: 286.348\n",
      "Cost: 153.858\n",
      "Cost: 210.456\n",
      "Cost: 236.955\n",
      "Cost: 236.724\n",
      "Cost: 178.825\n",
      "Cost: 266.192\n",
      "Cost: 119.447\n",
      "Cost: 175.057\n",
      "Cost: 209.548\n",
      "Cost: 241.940\n",
      "Cost: 160.880\n",
      "Cost: 270.824\n",
      "Cost: 195.387\n",
      "Cost: 191.301\n",
      "Cost: 205.250\n",
      "Cost: 239.187\n",
      "Cost: 219.441\n",
      "Cost: 168.914\n",
      "Cost: 189.310\n",
      "Cost: 188.218\n",
      "Cost: 217.161\n",
      "Cost: 176.616\n",
      "Cost: 214.811\n",
      "Cost: 175.277\n",
      "Cost: 220.515\n",
      "Cost: 186.706\n",
      "Cost: 252.329\n",
      "Cost: 180.589\n",
      "Cost: 191.276\n",
      "Cost: 217.249\n",
      "Cost: 200.937\n",
      "Cost: 197.251\n",
      "Cost: 208.918\n",
      "Cost: 272.913\n",
      "Cost: 203.065\n",
      "Cost: 215.984\n",
      "Cost: 198.326\n",
      "Cost: 188.218\n",
      "Cost: 307.453\n",
      "Cost: 183.889\n",
      "Cost: 168.911\n",
      "Cost: 190.859\n",
      "Cost: 201.625\n",
      "Cost: 250.165\n",
      "Cost: 153.570\n",
      "Cost: 237.959\n",
      "Cost: 203.081\n",
      "Cost: 209.177\n",
      "Cost: 143.373\n",
      "Cost: 188.683\n",
      "Cost: 212.269\n",
      "100/|/100%|| 100/100 [00:00<00:00,  9.97it/s]\n",
      "212.269\n"
     ]
    }
   ],
   "source": [
    "with tqdm_notebook(total=100, desc=\"Cost\") as pbar:\n",
    "    for i in xrange(100):\n",
    "        outputs = sess.run([model.optimizer, model.cost], {model.data: x,\n",
    "                                                           model.labels: y,\n",
    "                                                           model.dropout: 0.1})\n",
    "        pbar.update(1)\n",
    "        pbar.write(\"Cost: %.3f\" % outputs[1])\n",
    "print outputs[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "212.269\n"
     ]
    }
   ],
   "source": [
    "print outputs[1]\n",
    "prediction = sess.run(model.predictions, {model.data: x, model.labels: y, model.dropout: 0.1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 100, 1)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YQWZmJh9++CHbt28nPT2dNWvWMGLECAAmT57M0KFDy68fOHAg3377La+88gq//PILmzZt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\njx6E+Mkk7dq1i9TUVG655ZZq/yckUp8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d08311b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,5, sharex=True, sharey=False, figsize=(15, 6))\n",
    "ax = ax.flatten()\n",
    "for i in xrange(x.shape[0]):\n",
    "    ax[i].plot(x[i, :, 0], y[i, :, 0], \"b-\")\n",
    "    ax[i].plot(x[i, :, 0], prediction[i, :, 0], \"r--\")\n",
    "    \n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
